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The rise in hydrological and physiographic spatiotemporal data has boostedmachine learning's role in rapid flood mapping. Yet, data scarcity, especiallyhigh-resolution DEMs, challenges regions with limited resources. This paperexamines how DEM type and resolution affect flood prediction accuracy,utilizing a cutting-edge deep learning (DL) method called 1D convolutionalneural network (CNN). It utilizes synthetic hydrographs for training input andwater depth data from LISFLOOD-FP, a 2D hydrodynamic model, for target data.This study investigates digital surface models (DSMs) and digital terrainmodels (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to30 m. The methodology is applied and assessed in the established benchmark siteof Carlisle, UK. The models' performance is then evaluated and compared againstan observed flood event using RMSE, Bias, and Fit indices. Leveraging theinsights gained from this region, the paper discusses the applicability of themethodology to address the challenges encountered in a data-scarce flood-proneregion, exemplified by Pakistan. Results indicated that utilizing a 30 m DTMimproved flood depth prediction accuracy by about 21% during the flood peakstage compared to a 30 m DSM, highlighting the superior performance of DTM atlower resolutions. Increasing the resolution of DTM to 15 m resulted in aminimum 50% increase in RMSE and a 20% increase in fit index across all floodphases. The findings emphasize that while a coarser resolution DEM may impactthe accuracy of machine learning models, it remains a viable option for rapidflood prediction. Any attempts to improve data resolution in data-scarceregions would provide significant added value, ultimately enhancing flood riskmanagement.

Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping